Paleopathology

1
The history of paleopathology from small to
large numbers

• Stage I Case Studies
• Dominated almost the end of the 20th century
• Physician to the dead approach
• century took a descriptive, case study
• Emphasis on determining the spatial temporal
  distribution of diseases.
• Stage II Population Studies
• Mainly during the last 50 years.
• Emphasis on calculating the prevalence of common
  pathological conditions in cemetery collections
• Bioarchaeological approach with an emphasis on
  cultural and ecological determinants of health
  status

2
Goals of Modern Paleopathology

• Describe the chronology and spatial distribution
  of health-related conditions in an earlier
  populations
• Determine the biocultural interactions that occur
  as a population responds to its environment,
  using disease as an index of the success or
  failure of adaptation
• Use the prevalence and pattern of disease to shed
  light on the adaptation of the population
• Investigate the processes involved in prehistoric
  the evolution of ancient diseases

3
What are the limitations of apopulation-based
approach in paleopathology?

• How large are the samples that we will need to
  detect population differences we might reasonably
  expect to see in the frequency of pathological
  conditions?
• How significant are sample biases introduced by
  age, sex, and preservation differences between
  samples?
• What problems are there with pooling samples from
  different sites to increase sample sizes?

4
Western Hemisphere and History of Health in
Europe Project Sites893 sites, total n 142,952
5
Most archaeological skeletal collections are
small!
6
Most archaeological skeletal collections are
small!
7
Cemetery collections from archaeological
sitesmedian 59, mode 1
8
Number of skeletons required to detect a
statistically significant difference in the
proportion of people afflicted with a
pathological condition
9
Cutting up the Pie Makes Things Worse!

• Testing bioarchaeological hypotheses typically
  requires subdividing site samples
• Age
• Sex
• Social Status

10
Sex is a big part of the pie!

• 39.8 of burials in the Western Hemisphere
  sample are younger than 15 years old and thus
  probably not subject to reliable sex
  determination.

11
The real world situation is worse..

• Only 41 of the Western Hemisphere sample could
  be sexed to the level of probable male or
  probable female.
• This means that about 24 burials in a sample with
  the median size of 59 can be reliably sexed.
• Assuming a balanced sex ratio, this would mean
  that within-site sex comparisons would typically
  involve 12 males and 12 females

12
Age
Subadults 59 x 0.38 22 Adults 59 x 0.62 37
13
The effects of preservation biases can be
significant!
14
How should frequencies of pathological lesions be
measured?
15
The under-representation of pathological
conditions in skeletal samples

• Many diseases such as tuberculosis only leave
  lesions on a small proportion of individuals
• Many lethal injuries leave no skeletal traces
• Poor preservation of ancient skeletal material
  means that often subtle signs of disease and
  traumatic injury will either be unobservable or
  uninterpretable

16
What can large samples tell us?
17
A Caveat variation among contemporaneous
populations within a region can be significant
18
Variations in the bathtub curve

• Wide differentials in the excess mortality
  occurring at the youngest and oldest ages
• Marked differences in the timing of the decline
  in juvenile mortality or the rise in adult
  mortality

19
Could we detect minor variations in the bathtub
curve?

• The adolescent accident hump between ages
  15-18.
• Apparent slowing down of the rate of increase of
  mortality among the oldest of the old

20
What are our chances of detecting the Basic
human mortality pattern?

• The bathtub curve this is a species-wide theme
  in human mortality
• Basic features
• Excess mortality at the youngest ages of the life
  span
• Rapid decline to a lifetime low at around 10-15
  years of age
• Accelerating, roughly exponential, rise in
  mortality at later ages

21
Age Related Changes in Bones Mass
22
Conclusions

• Small sample sizes and preservation biases mean
  that paleodemographers will never be able to
  reconstruct the fine details of any set of
  mortality rates.
• At best, we can hope to learn something about the
  overall level and age pattern of death in the
  distant past - and perhaps something about the
  gross differences in material conditions that led
  to variation in level and age pattern.
• Paleodemographers will probably never be able to
  reconstruct the "bumps and squiggles" in ancient
  mortality patters.
• Reconstructing the general shape and level of the
  bathtub curve will be challenging enough.

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Regional Variation
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Bioarchaeologically Interesting Differences

• Time how does health status vary through time
• Space What regional or intraregional differences
  are there?
• Age What is the relationship between age at
  death and the presence of pathological lesions
  indicative of a specific disease?
• Sex how does a persons sex influence their
  health status?
• Social Status How do social stratification and
  gender roles influence health status?

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Osteoperiostitis
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Osteoperiostitis
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Long Bones Affected
28
Temporal Variation
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Statistical Power

• The probability of rejecting a false statistical
  null hypothesis.
• Performing power analysis and sample size
  estimation is an important aspect of experimental
  design, because without these calculations,
  sample size may be too high or too low.
• If sample size is too low, the experiment will
  lack the precision to provide reliable answers to
  the questions it is investigating.
• If sample size is too large, time and resources
  will be wasted, often for minimal gain.

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Determining Sample Size

• What kind of statistical test is being performed.
  Some statistical tests are inherently more
  powerful than others.
• Sample size. In general, the larger the sample
  size, the larger the power.
• However, generally increasing sample size
  involves tangible costs, both in time, money, and
  effort.
• Consequently, it is important to make sample size
  "large enough," but not wastefully large.
• In paleopathological studies increasing sample
  size is typically impossible
• The size of experimental effects. If the null
  hypothesis is wrong by a substantial amount,
  power will be higher than if it is wrong by a
  small amount.
• The level of error in experimental measurements.
  Measurement error acts like "noise" that can bury
  the "signal" of real experimental effects.
  Consequently, anything that enhances the accuracy
  and consistency of measurement can increase

31

• alpha specifies the significance level of the
  test the default is alpha (.05).
• power() is power of the test. Default is
  power(.90).

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Age determination is a blunt sword
33
A priori sample size estimation

• Based on the acceptable statistical significance
  of your outcome measure.
• Specify the smallest effect you want to detect of
  the Type I and Type II error rates

34
Error Types

• Type 1 error The chance of accepting the
  research hypothesis when the null hypothesis is
  actually true ("false positive").
• Type 2 error The chance of accepting the null
  hypothesis when the research hypothesis is
  actually true ("false negative").